This disclosure relates generally to generating routes for vehicles in general, and more particularly to generating accurate routes based on high definition maps with high precision for safe navigation of autonomous vehicles.
Autonomous vehicles, also known as self-driving cars, driverless cars, auto, or robotic cars, drive from a source location to a destination location without requiring a human driver to control and navigate the vehicle. Automation of driving is difficult due to several reasons. For example, autonomous vehicles use sensors to make driving decisions on the fly, but vehicle sensors cannot observe everything all the time. Vehicle sensors can be obscured by corners, rolling hills, and other vehicles. Vehicles sensors may not observe certain things early enough to make decisions. In addition, lanes and signs may be missing on the road or knocked over or hidden by bushes, and therefore not detectable by sensors. Furthermore, road signs for rights of way may not be readily visible for determining from where vehicles could be coming, or for swerving or moving out of a lane in an emergency or when there is a stopped obstacle that must be passed.
Autonomous vehicles can use map data to figure out some of the above information with high confidence instead of relying on less-reliable sensor data. However conventional maps have several drawbacks that make them difficult to use for an autonomous vehicle. To be useful, the geometry of the map and the ability of the vehicle to determine its location in the map needs to be highly accurate (e.g., 10 cm or less). Conventional maps do not provide the level of accuracy required for safe navigation. GPS systems provide accuracies of approximately 3-5 meters, but have large error conditions resulting in an accuracy of over 100 m which occur frequently depending on environmental conditions. This makes it challenging to accurately determine the location of the vehicle with a conventional map and GPS.
Furthermore, conventional maps are created by survey teams that use drivers with specially outfitted cars with high resolution sensors that drive around a geographic region and take measurements. The measurements are taken back and a team of map editors assembles the map from the measurements. This process is expensive and time consuming (e.g., taking possibly months to complete a map). Therefore, maps assembled using such techniques do not have fresh data. For example, roads are updated/modified on a frequent basis roughly 5-10% per year. But survey cars are expensive and limited in number, so cannot capture most of these updates. For example, a survey fleet may include a thousand cars. For even a single state in the United States, a thousand cars would not be able to keep the map up-to-date on a regular basis to allow safe self-driving. As a result, conventional techniques of maintaining maps are unable to provide the right data that is sufficiently accurate and up-to-date for safe navigation of autonomous vehicles.